Hawks is the Vilas-Borghesi Distinguished Achievement Professor of Anthropology at the University of Wisconsin – Madison. He is an anthropologist and studies the bones and genes of ancient humans. He’s worked on almost every part of our evolutionary story, from the very origin of our lineage among the apes, to the last 10,000 years of our history.

In mathematics, a manifold is a topological space that locally resembles Euclidean space near each point.

Steve Hsu and Corey Washington have been friends for almost 30 years, and between them hold PhDs in Neuroscience, Philosophy, and Theoretical Physics. Join them for wide ranging and unfiltered conversations with leading writers, scientists, technologists, academics, entrepreneurs, investors, and more.

Steve Hsu is VP for Research and Professor of Theoretical Physics at Michigan State University. He is also a researcher in computational genomics and founder of several Silicon Valley startups, ranging from information security to biotech. Educated at Caltech and Berkeley, he was a Harvard Junior Fellow and held faculty positions at Yale and the University of Oregon before joining MSU.

Corey Washington is Director of Analytics in the Office of Research and Innovation at Michigan State University. He was educated at Amherst College and MIT before receiving a PhD in Philosophy from Stanford and a PhD in a Neuroscience from Columbia. He held faculty positions at the University Washington and the University of Maryland. Prior to MSU, Corey worked as a biotech consultant and is founder of a medical diagnostics startup.

1. 13 year olds identified through talented and gifted programs, all of whom scored in the top 1% in at least one of Mathematical or Verbal ability (based on SAT score; some scored at the 1 in 10k level). They were also assessed using a preference inventory (SOV = Study of Values). About 10% of this cohort of 677 were identified 35 years later as having achieved "eminence" in their careers -- e.g., full professor at R1 university, senior executive status, ...

2. Exceptional STEM graduate students at top 15 PhD programs, evaluated using GRE and SOV. If I'm not mistaken many or all of these students were NSF Graduate Fellows. About 20% of this population of 605 had achieved STEM eminence 25 years later.

I would estimate that only about one in a thousand individuals drawn randomly from the general population attains eminence as defined in the paper. Thus, the talent selection used to form cohorts 1&2 (e.g., SAT administered at age 13) produced success rates as much as 100 times higher than in the base population.

Abstract
This investigation examined whether math/scientific and verbal/humanistic ability and preference constellations, developed on intellectually talented 13-year-olds to predict their educational outcomes at age 23, continue to maintain their longitudinal potency by distinguishing distinct forms of eminence 35 years later. Eminent individuals were defined as those who, by age 50, had accomplished something rare: creative and highly impactful careers (e.g., full professors at research-intensive universities, Fortune 500 executives, distinguished judges and lawyers, leaders in biomedicine, award-winning journalists and writers). Study 1 consisted of 677 intellectually precocious youths, assessed at age 13, whose leadership and creative accomplishments were assessed 35 years later. Study 2 constituted a constructive replication—an analysis of 605 top science, technology, engineering, and math (STEM) graduate students, assessed on the same predictor constructs early in graduate school and assessed again 25 years later. In both samples, the same ability and preference parameter values, which defined math/scientific versus verbal/humanistic constellations, discriminated participants who ultimately achieved distinct forms of eminence from their peers pursuing other life endeavors.

Note that even within both cohorts SAT / GRE were useful in predicting achievement outcomes. Click figures below for larger versions.

Wednesday, March 13, 2019

DNA forensics -- the use of DNA for identification of criminals, victims, military remains, etc. -- will also be transformed by inexpensive genotyping and powerful informatics.

The existing FBI standard (CODIS) for DNA identification uses only 20 markers (STRs -- previously only 13 loci were used!). By contrast, genome wide sequencing can reliably call millions of genetic variants. For the first time, the cost curves for these two methods have crossed: modern sequencing costs no more than extracting CODIS markers using the now ~30 year old technology.

What can you do with millions of genetic markers?

1. Determine relatedness of two individuals with high precision. This allows detectives to immediately identify a relative (ranging from distant cousin to sibling or parent) of the source of the DNA sample, simply by scanning through large DNA databases. In many cases this narrows the pool of suspects to ~100 or fewer individuals. With only 20 CODIS markers this is not possible. Some notorious cold cases have already been solved using this method, with more examples every few weeks.

2. Phenotype and Ancestry reports: in addition to ethnicity, we can now predict cosmetic traits such as hair color, eye color, skin tone (i.e., light to dark), baldness, height, BMI, and bodyfat percentage. (The last two are the least accurate, although outlier ares still identifiable.) Again, not remotely possible using CODIS markers.

I'm a co-founder of Othram, a startup providing 1&2 above to law enforcement, the military, and other customers.

Recently I visited Othram's brand new 4000 square foot lab which will be entirely dedicated to forensic applications of advanced sequencing and genomic prediction. The lab has specialized air handling and sample processing infrastructure, and will soon be home to an Illumina NovaSeq.

On the legal status of large DNA databases, such as those of 23andMe and Ancestry: these firms have genotyped 5M and 10M individuals, respectively, with both numbers set to double in the next year. These datasets are large enough to, e.g., immediately return a first- or second-cousin match for most searches on DNA from someone of primarily European heritage. With such resources the majority of crimes with DNA evidence become easy to solve. The Genomic Panopticon is nearly a reality.

Both 23andMe and Ancestry have, on grounds of customer privacy, resisted law enforcement requests to search for matches to forensic DNA. However, one of their smaller competitors, FamilyTreeDNA, revealed that it is routinely collaborating with FBI. I do not believe that 23andMe or Ancestry can resist a court order if vigorously pursued. The situation is similar to that of ISPs and web email providers in the early days of the internet. They also resisted cooperation with law enforcement on privacy grounds, but in the end had to capitulate. All such firms today have compliance departments that process law enforcement queries on a routine basis. I would be very surprised if 23andMe and Ancestry don't end up with a similar accommodation, despite their current posture.

Thursday, March 07, 2019

Kaiser Kuo is a host and co-founder of Sinica, a current affairs podcast originally based in Beijing. Sinica guests include prominent journalists, academics, and policy makers who participate in uncensored discussions about Chinese political, economic, and cultural affairs.

In mathematics, a manifold is a topological space that locally resembles Euclidean space near each point.

Steve Hsu and Corey Washington have been friends for almost 30 years, and between them hold PhDs in Neuroscience, Philosophy, and Theoretical Physics. Join them for wide ranging and unfiltered conversations with leading writers, scientists, technologists, academics, entrepreneurs, investors, and more.

Steve Hsu is VP for Research and Professor of Theoretical Physics at Michigan State University. He is also a researcher in computational genomics and founder of several Silicon Valley startups, ranging from information security to biotech. Educated at Caltech and Berkeley, he was a Harvard Junior Fellow and held faculty positions at Yale and the University of Oregon before joining MSU.

Corey Washington is Director of Analytics in the Office of Research and Innovation at Michigan State University. He was educated at Amherst College and MIT before receiving a PhD in Philosophy from Stanford and a PhD in a Neuroscience from Columbia. He held faculty positions at the University Washington and the University of Maryland. Prior to MSU, Corey worked as a biotech consultant and is founder of a medical diagnostics startup.

Saturday, March 02, 2019

Techno-pessimists should note that detecting gravity waves is much, much harder than landing on the moon. LIGO measured a displacement 1/1000 of a neutron radius, in a noisy terrestrial background, accounting even for quantum noise.

Here is the paper http://journals.aps.org/prl/abstract/10.1103/PhysRevLett.116.061102

When I was an undergraduate, I toured the early LIGO prototype, which was using little car shaped rubber erasers as shock absorbers. Technology has improved since then, and the real device is much bigger.

As Kip makes clear in his talk, the detection of gravity waves was a ~50 year project involving large numbers of very smart physicists and engineers, with the sustained support of some of the most impressive scientific institutions in the world (Caltech, MIT, NSF, Moscow State University). Entirely new technologies and areas of theoretical and experimental physics had to be developed to bring this dream to fruition.

Wednesday, February 27, 2019

Wikipedia: Motoo Kimura (木村 資生 Kimura Motō) (November 13, 1924 – November 13, 1994) was a Japanese biologist best known for introducing the neutral theory of molecular evolution in 1968.[2][3] He became one of the most influential theoretical population geneticists. He is remembered in genetics for his innovative use of diffusion equations to calculate the probability of fixation of beneficial, deleterious, or neutral alleles.[4] Combining theoretical population genetics with molecular evolution data, he also developed the neutral theory of molecular evolution in which genetic drift is the main force changing allele frequencies.[5] James F. Crow, himself a renowned population geneticist, considered Kimura to be one of the two greatest evolutionary geneticists, along with Gustave Malécot, after the great trio of the modern synthesis, Ronald Fisher, J. B. S. Haldane and Sewall Wright.[6]

What is the fate of the neutral theory? I suppose the fundamental question is what fraction of molecular changes (mutations) have significant phenotypic effects (i.e., effects on fitness). If the fraction is very small then one could, at the molecular level, adopt the neutral theory as a first approximation. (At the level of phenotypes, I can't see drift dominating unless the effective population size is very small.) Still unsettled?

Wikipedia: ... According to Kimura, the theory applies only for evolution at the molecular level, and phenotypic evolution is controlled by natural selection, as postulated by Charles Darwin. The proposal of the neutral theory was followed by an extensive "neutralist-selectionist" controversy over the interpretation of patterns of molecular divergence and polymorphism, peaking in the 1970s and 1980s. Since then, much evidence has been found for selection at molecular level.

... Kimura was originally trained as a plant cytologist; he had been fascinated by plants since boyhood, and cytogenetics had been the hot field in Japan at the time. But his interest in chromosomes waned as he began yearning to “do something in genetics like what the theoretical physicists were doing in physics.” This ambition was buoyed by Kimura’s regular, hunger-fueled excursions to the house of his cousin-in-law Matsuhei Tamura, a mathematical physicist. Kimura visited almost every Sunday, partly because he was intensely interested in the quantum physicist’s stories, and partly because he needed to fill his belly during the post-war food shortages.

Kimura joined the lab of Japan’s most famous cytogeneticist, Hitoshi Kihara, who recognized the quiet young man’s talent for theory and left him mostly to his own devices. So, while his friends picked apart the chromosomes of wheat and watermelon, Kimura indulged in the more abstract pleasures of population genetics. He would travel the full-day’s train journey to Tokyo to copy out by hand the papers of Sewall Wright, one of the founders of the field. Determined to understand Wright’s papers, Kimura haunted the math department, attending classes, asking questions, learning from books, until he gradually gained the sophistication to follow Wright’s arguments, and eventually, critique and extend them.

But this new intellectual world was isolating. Kimura’s lab mates took a dim view of his absorption in mathematics and the situation only worsened when he took a job at the newly founded National Institute of Genetics. The facility was housed in the makeshift and uncomfortable office of a wartime aircraft factory. There was no library, no access to foreign journals, and no colleague who could understand his work. The only geneticist there who saw its value was zoologist Taku Komai, who had studied in the fly lab of genetics superstar T. H. Morgan in the United States. Komai recommended Kimura extend his training overseas and introduced him to an American scientist working for the Atomic Bomb Casualty Commission. Before long Kimura had a scholarship, a Fulbright travel award, and a ticket to Seattle.

Once they met, Crow immediately took Kimura under his wing. He invited Kimura over for dinner to meet his idol Sewall Wright. Crow probed Kimura about the paper he had just written on the Pacific voyage and was impressed that it neatly reduced a formidably complex equation down to a simple relationship used by physicists to describe heat conduction. He encouraged Kimura to submit the paper to GENETICS, where Crow was an editor (the paper was later effusively and uncharacteristically praised by its reviewer, Wright).

Britain could contribute huge value to the world by leveraging existing assets, including scientific talent and how the NHS is structured, to push the frontiers of a rapidly evolving scientific field — genomic prediction — that is revolutionising healthcare in ways that give Britain some natural advantages over Europe and America. We should plan for free universal ‘SNP’ genetic sequencing as part of a shift to genuinely preventive medicine — a shift that will lessen suffering, save money, help British advanced technology companies in genomics and data science/AI, make Britain more attractive for scientists and global investment, and extend human knowledge in a crucial field to the benefit of the whole world.

Those that are interested in the history of science, or in understanding its future, would do well to look at what was being written 10 or so years ago about genomics of complex traits. Whose predictions came true? Whose were dead wrong?

Dominic Cummings: ... Hsu predicted that very large samples of DNA would allow scientists over the next few years to start identifying the actual genes responsible for complex traits, such as diseases and intelligence, and make meaningful predictions about the fate of individuals. Hsu gave estimates of the sample sizes that would be needed. His 2011 talk contains some of these predictions and also provides a physicist’s explanation of ‘what is IQ measuring’. As he said at Google in 2011, the technology is ‘right on the cusp of being able to answer fundamental questions’ and ‘if in ten years we all meet again in this room there’s a very good chance that some of the key questions we’ll know the answers to’. His 2014 paper explains the science in detail. If you spend a little time looking at this, you will know more than 99% of high status economists gabbling on TV about ‘social mobility’ saying things like ‘doing well on IQ tests just proves you can do IQ tests’.

In 2013, the world of Westminster thought this all sounded like science fiction and many MP said I sounded like ‘a mad scientist’. Hsu’s predictions have come true and just five years later this is no longer ‘science fiction’. (Also NB. Hsu’s blog was one of the very few places where you would have seen discussion of CDOs and the 2008 financial crash long BEFORE it happened. I have followed his blog since ~2004 and this from 2005, two years before the crash started, was the first time I read about things like ‘synthetic CDOs’: ‘we have yet another ill-understood casino running, with trillions of dollars in play’. The quant-physics network had much better insight into the dynamics behind the 2008 Crash than high status mainstream economists like Larry Summers responsible for regulation.)

His group and others have applied machine learning to very large genetic samples and built predictors of complex traits. Complex traits like general intelligence and most diseases are ‘polygenic’ — they depend on many genes each of which contributes a little (unlike diseases caused by a single gene).

‘There are now ~20 disease conditions for which we can identify, e.g, the top 1% outliers with 5-10x normal risk for the disease. The papers reporting these results have almost all appeared within the last year or so.’

(One might ask what fraction of PhD economists knew in 2008 what a CDO was or how they were constructed or priced... I was there, and the answer is: very, very few.)

... machine learning is itself based on accurate credit assignment. Good learning algorithms assign higher weights to features or signals that correctly predict outcomes, and lower weights to those that are not predictive. His analogy between science itself and machine learning is often lost on critics.

Therefore, to decide how to weight current claims about the future (such as: accurate genomic prediction of many disease risks and complex traits, even including cognitive ability, are right around the corner), one should carefully study the track record of those offering predictions.

Friday, February 22, 2019

When I was in London recently I recorded an interview with editor Tom Standage of The Economist. It's now been released as an Economist Radio podcast. (Apologies, I don't have embed code, but the link above will take you to the audio.)

Corey and Steve speak with Ted Shultz, research Entomologist at the Smithsonian National Museum of Natural History. Ted is an expert in Leaf Cutter Ant evolution and systematics. Topics discussed include evolution, systematics, the genetic basis of behavior, E. O. Wilson and small revolutions in science.

In mathematics, a manifold is a topological space that locally
resembles Euclidean space near each point.

Steve Hsu and Corey Washington have been friends for almost 30 years, and between them hold PhDs in Neuroscience, Philosophy, and Theoretical Physics. Join them for wide ranging and unfiltered conversations with leading writers, scientists, technologists, academics, entrepreneurs, investors, and more.

Steve Hsu is VP for Research and Professor of Theoretical Physics at Michigan State University. He is also a researcher in computational genomics and founder of several Silicon Valley startups, ranging from information security to biotech. Educated at Caltech and Berkeley, he was a Harvard Junior Fellow and held faculty positions at Yale and the University of Oregon before joining MSU.

Corey Washington is Director of Analytics in the Office of Research and Innovation at Michigan State University. He was educated at Amherst College and MIT before receiving a PhD in Philosophy from Stanford and a PhD in a Neuroscience from Columbia. He held faculty positions at the University Washington and the University of Maryland. Prior to MSU, Corey worked as a biotech consultant and is founder of a medical diagnostics startup.

Monday, February 18, 2019

Overly aggressive US foreign and economic policies toward Russia and China are pushing the two into a tighter relationship. US rapprochement with China, exploiting Sino-Soviet tensions, was an important achievement of Nixon and Kissinger in the previous Cold War. Today a solidified Russia-China bloc is an extremely negative development for US interests.

There are important synergies between the two countries. Russia still leads in key military technologies, and can supply China with badly needed natural resources. China has a more vibrant economy and is starting to surge into global leadership across a range of technologies and in manufacturing.

The main source of potential conflict between the two is the sparsely populated, but resource rich, Russian Far East. I doubt territorial ambitions there are a top priority for China, especially if an amicable trading relationship can be established for oil, gas, and other resources. Chinese economic influence in the region is growing, threatening to overwhelm the Russians. But the trajectory is manageable if both sides agree to cooperate.

The article excerpted below is by Bruno Maçães, a former Europe minister for Portugal, and author of The Dawn of Eurasia (Penguin 2018).

Politico (EU): ... In the halls of the Kremlin these days, it’s all about China — and whether or not Moscow can convince Beijing to form an alliance against the West.

Russia’s obsession with a potential alliance with China was already obvious at the Valdai Discussion Club, an annual gathering of Russia’s biggest foreign policy minds, in 2017.

At their next meeting, late last year, the idea seemed to move from the speculative to something Russia wants to realize. And soon.

... Every Russian speech — from obscure academics to Foreign Minister Sergei Lavrov and Russian President Vladimir Putin himself — played that note and no other. There was even a new sense of desperation in the air.

As Sergey Karaganov, a former adviser to Putin, explained to me at breakfast, now everything must be about China.

... There was no doubt at Valdai that China knows how to do economic growth, and that Russia does not. Russia’s elite — always so ready to resist any sign of Western hegemony — have no problem admitting China’s economic superiority. Their acceptance reminded me of the way Britain gave way to the United States as the world’s dominant economic power.

... In the past, the possibility of an alliance between the two countries had been hampered by China’s reluctance to jeopardize its relations with the U.S. But now that it has already become a target, perhaps it will grow bolder. Every speaker at Valdai tried to push China in that direction.

When Putin finished a fireside chat with policymakers — a set-piece of the conference, where he fields softball questions from the audience — he made a gesture to leave the room, but then quickly rushed back to grab Yang Jiechi, a former Chinese foreign minister and arguably the main architect of the country’s foreign policy. He insisted on walking out with Yang by his side, to the obvious delight of his Chinese guest.

... I met Karaganov again at a meeting with Chinese officials and think tankers in Beijing a few weeks ago. There, a number of Chinese participants said they doubted Russia’s assertions that the world is in the midst of a new Cold War.

Karaganov dedicated himself to convincing them otherwise, arguing with increasing passion that China is deluding itself if it thinks issues between Beijing and Washington can be conveniently resolved to the benefit of both sides.

If Beijing places its bets on peace and cooperation, the great Chinese adventure will come to an end, and China will have to live in the shadow of the U.S. for another generation — perhaps forever, Karaganov said. Chinese authorities, he argued, have no more than five years to make a decision.

The meeting was held under the Chatham House rule, so unfortunately, I cannot report on what the response from the Chinese side was; only Karaganov allowed me to relay his words.

... from my own separate conversations, Chinese officials appear to agree the clock is ticking. They’re just not yet convinced they should choose war — even a Cold War.

First UFC victory by a Gracie since 1994! Congratulations to Kron, son of Rickson Gracie. His nickname, Ice Cream Kron, means Cool Under Pressure. Ironically, you can tell that he's a sensitive guy and that fighting takes a huge toll on him.

He won in old school fashion. The progression was classic -- something I taught to Yale BJJ club students in the mid-1990s. Caceres throws a right, Kron ducks under to get the clinch, hiding his head under Cacere's arm. Kron takes the back, entwines his leg and uses his bodyweight to take Caceres to the mat. Kron moves smoothly into a rear naked choke, hiding his hands from Caceres. Almost no energy expended by Kron. Alex Caceres, an athletic UFC veteran, defeated in 90 seconds with a minimum of violence.

Jiujitsu, the gentle art.

Some background on Rickson and Kron, from Eddie Bravo and Joe Rogan. Kron's submission grappling fights against Garry Tonon and Marcelo Garcia are unbelievable.

The UK could become the world leader in genomic research by combining population-level genotyping with NHS health records. The application of AI to datasets of this kind has already led to the creation of genomic predictors that can identify individuals at high risk for common disease conditions such as breast cancer, heart disease, diabetes, hypothyroidism, etc. Such breakthroughs provide insight into the genetics of disease, and allow more efficient allocation of resources for prevention and early detection to the individuals who would most benefit. This saves both lives and money.

The US private health insurance system produces the wrong incentives for this kind of innovation: payers are reluctant to fund prevention or early treatment because it is unclear who will capture the ROI. Consider a cost-effective intervention that, e.g., prevents a patient from developing diabetes. This produces an obvious health benefit, but if the patient later changes insurer the financial benefits are lost to the one that paid for the intervention. Not a problem, though, in a single-payer system.

The NHS has the right incentives, the necessary scale, and access to a deep pool of scientific talent. The UK can lead the world into a new era of precision genomic medicine.

NHS has already announced an out-of-pocket genotyping service which allows individuals to pay for their own genotyping and to contribute their health + DNA data to scientific research. In recent years NHS has built an impressive infrastructure for whole genome sequencing (cost ~$1k per individual) that is used to treat cancer and diagnose rare genetic diseases. The NHS subsidiary Genomics England recently announced they had reached the milestone of 100k whole genomes.

For common conditions such as heart disease, diabetes, breast cancer, etc., most of the recent advances in risk prediction have come from even larger datasets (many hundreds of thousands of individuals) with genotypes from inexpensive arrays (~$50; like those used by 23andMe) that sample the genome at the roughly million or so most informative locations. By contrast, a whole genome sequence measures all 3 billion base pairs. This provides more raw data per individual, but we do not currently know how to use most of that information.

At the meeting, I emphasized the following:

1. NHS should offer both inexpensive (~$50) genotyping (sufficient for risk prediction of common diseases) along with the more expensive $1k whole genome sequencing. This will alleviate some of the negative reaction concerning a "two-tier" NHS, as many more people can afford the former.

2. An in-depth analysis of cost-benefit for population wide inexpensive genotyping would likely show a large net cost savings: the risk predictors are good enough already to guide early interventions that save lives and money. Recognition of this net benefit would allow NHS to replace the $50 out-of-pocket cost with free standard of care.

Thursday, February 07, 2019

Corey and Steve interview Noor Siddiqui, a student at Stanford studying AI, Machine Learning, and Genomics. She was previously a Thiel Fellow, and founded a medical collaboration technology startup after high school. The conversation covers topics like college admissions, Tiger parenting, Millennials, Stanford, Silicon Valley startup culture, innovation in the US healthcare industry, and Simplicity and Genius.

man·i·fold /ˈmanəˌfōld/ many and various.

In mathematics, a manifold is a topological space that locally
resembles Euclidean space near each point.

Steve Hsu and Corey Washington have been friends for almost 30 years, and between them hold PhDs in Neuroscience, Philosophy, and Theoretical Physics. Join them for wide ranging and unfiltered conversations with leading writers, scientists, technologists, academics, entrepreneurs, investors, and more.

Steve Hsu is VP for Research and Professor of Theoretical Physics at Michigan State University. He is also a researcher in computational genomics and founder of several Silicon Valley startups, ranging from information security to biotech. Educated at Caltech and Berkeley, he was a Harvard Junior Fellow and held faculty positions at Yale and the University of Oregon before joining MSU.

Corey Washington is Director of Analytics in the Office of Research and Innovation at Michigan State University. He was educated at Amherst College and MIT before receiving a PhD in Philosophy from Stanford and a PhD in a Neuroscience from Columbia. He held faculty positions at the University Washington and the University of Maryland. Prior to MSU, Corey worked as a biotech consultant and is founder of a medical diagnostics startup.

We created new software. This was a gamble but the whole campaign was a huge gamble and we had to take many calculated risks. One of our central ideas was that the campaign had to do things in the field of data that have never been done before. This included a) integrating data from social media, online advertising, websites, apps, canvassing, direct mail, polls, online fundraising, activist feedback, and some new things we tried such as a new way to do polling (about which I will write another time) and b) having experts in physics and machine learning do proper data science in the way only they can – i.e. far beyond the normal skills applied in political campaigns. We were the first campaign in the UK to put almost all our money into digital communication then have it partly controlled by people whose normal work was subjects like quantum information ...

If you want to make big improvements in communication, my advice is – hire physicists, not communications people from normal companies and never believe what advertising companies tell you about ‘data’ unless you can independently verify it. Physics, mathematics, and computer science are domains in which there are real experts, unlike macro-economic forecasting which satisfies neither of the necessary conditions – 1) enough structure in the information to enable good predictions, 2) conditions for good fast feedback and learning. Physicists and mathematicians regularly invade other fields but other fields do not invade theirs so we can see which fields are hardest for very talented people. It is no surprise that they can successfully invade politics and devise things that rout those who wrongly think they know what they are doing. Vote Leave paid very close attention to real experts. (The theoretical physicist Steve Hsu has a great blog HERE which often has stuff on this theme, e.g. HERE.)

More important than technology is the mindset – the hard discipline of obeying Richard Feynman’s advice: ‘The most important thing is not to fool yourself and you are the easiest person to fool.’ They were a hard floor on ‘fooling yourself’ and I empowered them to challenge everybody including me. They saved me from many bad decisions even though they had zero experience in politics and they forced me to change how I made important decisions like what got what money. We either operated scientifically or knew we were not, which is itself very useful knowledge.

Thursday, January 31, 2019

Our plan is to release new episodes on Thursdays, at a rate of one every week or two.

We've tried to keep the shows at roughly one hour length -- is this necessary, or should we just let them go long?

Corey and Steve are joined by Bobby Kasthuri, a Neuroscientist at Argonne National Laboratory and the University of Chicago. Bobby specializes in nanoscale mapping of brains using automated fine slicing followed by electron microscopy. Among the topics covered: Brain mapping, the nature of scientific progress (philosophy of science), Biology vs Physics, Is the brain too complex to be understood by our brains? AlphaGo, the Turing Test, and wiring diagrams, Are scientists underpaid? The future of Neuroscience.

In mathematics, a manifold is a topological space that locally
resembles Euclidean space near each point.

Steve Hsu and Corey Washington have been friends for almost 30 years, and between them hold PhDs in Neuroscience, Philosophy, and Theoretical Physics. Join them for wide ranging and unfiltered conversations with leading writers, scientists, technologists, academics, entrepreneurs, investors, and more.

Steve Hsu is VP for Research and Professor of Theoretical Physics at Michigan State University. He is also a researcher in computational genomics and founder of several Silicon Valley startups, ranging from information security to biotech. Educated at Caltech and Berkeley, he was a Harvard Junior Fellow and held faculty positions at Yale and the University of Oregon before joining MSU.

Corey Washington is Director of Analytics in the Office of Research and Innovation at Michigan State University. He was educated at Amherst College and MIT before receiving a PhD in Philosophy from Stanford and a PhD in a Neuroscience from Columbia. He held faculty positions at the University Washington and the University of Maryland. Prior to MSU, Corey worked as a biotech consultant and is founder of a medical diagnostics startup.

The slides review the rapidly evolving situation in genomic prediction, focusing on disease risk predicted using inexpensive genotyping. There are now 10-20 disease conditions for which we can identify, e.g., the top 1% outliers with 5-10x normal risk for the disease. The papers reporting these results have almost all appeared within the last year or so!

On the last slide I give a simple cost-benefit analysis of population wide genotyping and conclude that the net benefit is already positive given the tools we have. The numbers used are per capita. The UK NHS is already headed in this direction.

I use breast cancer as the example on the slide, but since the same genotype can be used for 10+ disease risks (including diabetes, atrial fibrillation, hypothyroidism, etc.) the net benefit is potentially much larger than what is obtained from breast cancer alone. The point is that G is really small compared to the potential benefit.

Details of breast cancer calculation below. I am sure one can do much better, but it provides a quick back of the envelope estimate of the numbers.

Spend $100 per person to genotype all women in the population. Identify those with top 10% risk score. About 33% of these individuals will get breast cancer. Treat the risk outliers by giving them, e.g., regular mammograms starting a decade earlier than usual (~$100 annual mammogram x 10y = $1k). In the slide I assume the average cost of the intervention / treatment is $1k and the average benefit is $12k. All of the high risk women (10%) get the intervention, but only the 33% percent that get breast cancer (or some subset of that group) benefit from early detection. This paper estimates that early detection of breast cancer saves typically tens of thousands of dollars per individual, so my numbers are conservative. If one uses multiple tens of thousands as the benefit amount, one could spend much more on early treatment and still have a positive net benefit.

Thursday, January 24, 2019

Our plan is to record a new one every 1-2 weeks. We're in the process of scheduling now, so if you have contacted me to be on the show, or have suggested a guest, please bear with us as we get going.

Manifold man·i·fold /ˈmanəˌfōld/ many and various

In mathematics, a manifold is a topological space that locally resembles Euclidean space near each point.

Steve and Corey have been friends for almost 30 years, and between them hold PhDs in Neuroscience, Philosophy, and Theoretical Physics. Join them for wide ranging and unfiltered conversations with leading writers, scientists, technologists, academics, entrepreneurs, investors, and more.

Steve Hsu is VP for Research and Professor of Theoretical Physics at Michigan State University. He is also a researcher in computational genomics and founder of several Silicon Valley startups, ranging from information security to biotech. Educated at Caltech and Berkeley, he was a Harvard Junior Fellow and held faculty positions at Yale and the University of Oregon before joining MSU.

Corey Washington is Director of Analytics in the Office of Research and Innovation at Michigan State University. He was educated at Amherst College and MIT before receiving a PhD in Philosophy from Stanford and a PhD in a Neuroscience from Columbia. He held faculty positions at the University Washington and the University of Maryland. Prior to MSU, Corey worked as a biotech consultant and is founder of a medical diagnostics startup.

Advances in machine learning and genetic engineering are combining to produce rapid advances in medicine, development of materials and genetic engineering. Parallel advances in robotics and automation have made the practical process of gene editing scalable. The possibility exists that advances in quantum computing could further accelerate progress on machine learning, bringing a second boost to this technological rocket.

This Ditchley conference will bring together an unusual mix of deep expertise and scientific renown in the disciplines; thinkers on religion, ethics and law; investors fueling innovation; and political leaders looking to shape the approach of society and state to fast emerging possibilities. We will attempt to establish sufficient common understanding of what the science promises and what it doesn’t and then explore the opportunities and risks that are likely to unfold at speed. This will be a first pass at preparation for potential blast off – what should be our moral, legal, economic and national security checklist as we wait on the launch pad of a new age?

The progress on machine learning is quite narrow in scope – deep learning using neural networks and other techniques on large data sets that now exist that didn’t previously and that are store-able and computable in a way that was not possible previously. But whereas progress towards general AI is often overstated, full general AI is not required to radically accelerate gene sequencing, editing and programming, with costs falling all the time and scale and speed increasing.

We will examine and try to come to preliminary conclusions on questions such as the following:

How should the most aggressive genetic engineering technologies be regulated?

How can societies best assess the ethical issues raised by these technologies to find an optimal balance between fostering genetic technologies for the common good while preventing abuse?

What are the implications for the global economy and economic cooperation and competition between states? Are we entering a period of bio-nationalism as well as AI nationalism? Should this be compared to the space race of the Cold War? How can we avoid competition between states driving abandonment of norms and moral standards? What will be the impact on the labour force of the new combined technologies of AI and bio-engineering? Within countries, will potential applications of the new technologies further intensify the concentration of wealth and power in a few hands?

What are the implications of rapid combined advances in AI and bioengineering for defence and national security? Will countries be tempted to pursue military applications either through bio-weapons or through the genetic improvement of military forces? What new materials will emerge and how will they affect the balance of power in warfare?

What are the implications for medicine and public health? If we are able to find targeted genetic cures for diseases like cancer then what will the impact be on the population? What are the implications for ageing or declining populations?

How should we handle the implications of deeper knowledge about the influence of our genes on our characteristics and on the characteristics of groups? How do we chart a course between remaining scientifically objective and providing material that could be misused to support racist conclusions by those tending to that view?

What opportunities and threats are there in the potential of these combined technologies for democracies and the equal value put on the view point of each citizen in the electoral system and the rule of law? More philosophically, how can we make sure the development of these technologies contributes to a positive sense of human progress and meaning, rather than to a sense of alienation and loss of purpose? How can we manage the tension between science and religion as human capability to shape the genetic world increases?

I'm only briefly in London on my way there, but might be able to squeeze in a few meetings :-)

Thursday, January 17, 2019

On my last trip to London I learned that the startup Bablyon Health is causing a huge stir. Babylon received some initial funding from the founders of DeepMind (leading AI company acquired by Google; I was visiting them to give a talk).

Babylon created an AI phone app (a chatbot) to gather information from patients. The AI does triage and directs the patient (depending on severity of situation) to a GP (General Practitioner). The GP interaction takes place over video chat. The AI also suggests diagnoses to the GP who sees the recommendations on their computer screen. In tests the AI performed similarly to an average human GP and better than the worst GPs.

Thursday, January 10, 2019

We've taped a first episode and are working on a few more. The whole thing is an experiment -- no telling whether it will work out, and no promises I'll have time to really do it right.

Please give me suggestions for people you'd like to see interviewed on the show. Or volunteer yourself if you have something to share. I think we'll probably allow pseudonymous guests, so your identity can be kept private.

I'm especially interested in knowledgeable people who could give us insight on

Silicon Valley (Big Tech and startups and VC)
Financial Markets
Academia (Good, Bad, and Ugly)
The View from Europe
The View from Asia (Life in PRC? Fear and Loathing of PRC?)
Frontiers of Science (AI, Genomics, Physics, ...)
Frontiers of Rationality
The Billionaire Life
MMA / UFC
What Millennials think us old folks don't understand
True things that you are not allowed to say
Bubbles that are ready to pop?
Under-appreciated Genius?
Overrated Crap and Frauds?

Sunday, January 06, 2019

POSTED ON JANUARY 4, 2019 BY SCOTT ALEXANDER
Due to rain, we’re switching to holding the meetup indoors at 3045 Shattuck Ave, Berkeley, 94705. There will be several floors of space available for overflow, so hopefully it won’t be too crowded. Thanks to Claire, REACH, and Event Horizon for setting this up.

Time is still 3:30 PM on Sunday, 1/6. There’s also a Facebook event here.

For the unfamiliar, Slate Star Codex is one of the best blogs on the planet, with a large devoted following of rationalists. Scott is an incredibly talented writer and thinker, and I envy him his readership and commentariat :-)

In Houellebecq on Tocqueville, Democracy, and Nietzsche (2015) I pointed out that most intellectuals and elites have been so strongly conditioned by the existing cultural hegemony that they cannot understand obvious realities about the world. In that case I referred specifically to Houellebecq's previous novel Soumission.

Events since 2015 -- Trump's election and populist movements in Europe -- have stimulated a vague (but distorted) understanding in the minds of brainwashed elites as to populist discontent, its causes and origins. The reaction of our "thought leaders" is to decry the (previously sacred) democratic process by which the masses exercise their limited influence on society.

Individuals who told me confidently before the election that Trump had no chance of winning now forget how wrong they were then. They continue to express great confidence in their understanding of world events and political/economic processes.

So few are capable of updating prior beliefs in the face of new information. So many are overconfident in their powers of rationality.

Houellebecq has shown again that he understands reality much better than his critics.

Guardian: Serotonin, the story of a lovesick agricultural engineer who writes trade reports for the French agriculture ministry and loathes the EU, has been hailed by the French media as scathing and visionary. The novel rails against politicians who “do not fight for the interests of their people but are ready to die to defend free trade”.

Written before the current gilets jaunes anti-government movement began blockading roundabouts and tollbooths across France, it features desperate farmers in Normandy who stage an armed blockade of roads amid police clashes.

... In a recent article for Harpers, Houellebecq lauded Donald Trump for his protectionist policies, calling him “one of the best American presidents I’ve ever seen”, and praised Brexit: “The British get on my nerves, but their courage cannot be denied.” Serotonin, which will be published in English in September, viciously criticises free trade.

Tocqueville (Democracy in America, chapter 6) ... covers the surface of society with a network of small complicated rules, minute and uniform, through which the most original minds and the most energetic characters cannot penetrate, to rise above the crowd. The will of man is not shattered, but softened, bent, and guided; men are seldom forced by it to act, but they are constantly restrained from acting. Such a power does not destroy, but it prevents existence; it does not tyrannize, but it compresses, enervates, extinguishes, and stupefies a people, till each nation is reduced to nothing better than a flock of timid and industrious animals, of which the government is the shepherd.

Soma, Serotonin, soft censorship of dangerous ideas -- call it what you will.